Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies

Journal Article · 09 May 2024 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stae886 · OSTI ID:2396804

Rezaie, Mehdi ; Ross, Ashley J. ; Seo, Hee-Jong ; Kong, Hui ; Porredon, Anna ; Samushia, Lado; Chaussidon, Edmond; Krolewski, Alex; de Mattia, Arnaud; Beutler, Florian ; Aguilar, Jessica Nicole; Ahlen, Steven; Alam, Shadab; Avila, Santiago ; Bahr-Kalus, Benedict ; Bermejo-Climent, Jose; Brooks, David; Claybaugh, Todd; Cole, Shaun; Dawson, Kyle ; de la Macorra, Axel; Doel, Peter; Font-Ribera, Andreu ; Forero-Romero, Jaime E. ; Gontcho, Satya Gontcho A. ; Guy, Julien; Honscheid, Klaus; Huterer, Dragan; Kisner, Theodore; Landriau, Martin; Levi, Michael; Manera, Marc; Meisner, Aaron ; Miquel, Ramon; Mueller, Eva-Maria; Myers, Adam; Newman, Jeffrey A.; Nie, Jundan; Palanque-Delabrouille, Nathalie; Percival, Will ; Poppett, Claire; Rossi, Graziano; Sanchez, Eusebio ; Schubnell, Michael; Tarlé, Gregory; Weaver, Benjamin Alan; Yèche, Christophe; Zhou, Zhimin; Zou, Hu

ABSTRACT We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter fNL. Our sample comprises over 12 million targets, covering 14 000 deg2 of the sky, with redshifts in the range 0.2 < z < 1.35. We identify Galactic extinction, survey depth, and astronomical seeing as the primary sources of systematic error, and employ linear regression and artificial neural networks to alleviate non-cosmological excess clustering on large scales. Our methods are tested against simulations with and without fNL and systematics, showing superior performance of the neural network treatment. The neural network with a set of nine imaging property maps passes our systematic null test criteria, and is chosen as the fiducial treatment. Assuming the universality relation, we find $$f_{\rm NL} = 34^{+24(+50)}_{-44(-73)}$$ at 68 per cent (95 per cent) confidence. We apply a series of robustness tests (e.g. cuts on imaging, declination, or scales used) that show consistency in the obtained constraints. We study how the regression method biases the measured angular power spectrum and degrades the fNL constraining power. The use of the nine maps more than doubles the uncertainty compared to using only the three primary maps in the regression. Our results thus motivate the development of more efficient methods that avoid overcorrection, protect large-scale clustering information, and preserve constraining power. Additionally, our results encourage further studies of fNL with DESI spectroscopic samples, where the inclusion of 3D clustering modes should help separate imaging systematics and lessen the degradation in the fNL uncertainty.

• Sponsoring Organization: USDOE
• Grant/Contract Number: SC0021165; SC0011840; SC0019091; SC0023241
• OSTI ID: 2396804
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 2 Vol. 532; ISSN 0035-8711
• Publisher: Oxford University PressCopyright Statement
• Country of Publication: United Kingdom
• Language: English